1. Field of the Invention
The present invention generally relates to systems such as radar, sonar and the like which derive information from signals returned by reflection from a target and, more particularly, to estimation of the velocity of a moving target or targets, especially in connection with high resolution imaging or profiling of radar targets.
2. Description of the Prior Art
The use of radar, sonar and other arrangements for detecting the location of objects by analysis of signals transmitted from a known location and reflected by a target has long been known and has become well-developed. At the present state of the art, imaging of a stationary target is possible to determine some of the physical characteristics of the target. Such imaging techniques are perhaps most fully developed in devices for producing images known as sonograms where the target is usually stationary and the imaging is done over a very short range and in an environment which is reasonably quiet or where noise is reasonably predictable. Another such field is that of seismic tomography in which the imaging range and noise may be greater but the imaged subsurface structure is, necessarily, stationary.
High resolution radar (HRR) or sonar imaging or profiling, which may be regarded as one-dimensional imaging along a direction which is radial to the radar transmitter/receiver, on the other hand, presents a much larger range to the target which is typically in motion and a much noisier environment. Particularly in marine environments, HRR profiling will usually involve a moving target and many noise sources such as wave surfaces which will also be in motion. Therefore, particularly in such applications, the extraction of an image or profile from a signal which may have a low signal to noise ratio is of particular concern.
In high resolution radar, the down-range resolution of the radar is generally selected to be smaller than the anticipated length of the object. The length of the radar's pulse, however, is set to be more than twice the object's length, and therefore, sea clutter as well as system noise is present in these down-range profiles.
Since noise is generally a more or less random phenomenon and relatively rapidly changing relative to a target, averaging of returned signals has generally been the technique of choice for reducing noise and increasing the signal to noise ratio of the detected return signal. Specifically, random variation of the noise component of signals, when averaged over a number of iterations or bursts when groups of signals are transmitted as a burst, causes the noise components to average to zero as the number of samples becomes large. Thus, the component of a returned signal which is attributable to reflections from a stationary target may be greatly enhanced with respect to noise since these signals will be largely invariant and will average to a non-zero value.
A substantial difficulty is encountered, however, where the target is moving. Even with a relatively noise-free environment, such movement will cause blurring of a high resolution image. In noisy environments, the enhancement of the signal to noise ratio by averaging is much less effective since the portion of the signal attributable to the target is also changing and generally tends to also average to zero. This is particularly true of radar and sonar applications where the returned signal reflects changes of position in both the amplitude and phase of the returned signals. Because of the complex nature of radar and sonar signals, the application of signal enhancement techniques for high resolution imaging has been particularly difficult. In general, averaging has remained the signal enhancement technique of choice with some variations applied thereto in order to lessen the deleterious effects of target motion.
Pre- and post-detection integration are two general methods for processing radar signals to reduce noise and clutter. Consider a series of down-range profiles that are generated as a function of time. If the target is stationary relative to the radar and the clutter sources are moving (reflections from ocean waves), then pre-detection integration (coherent averaging using first-order statistics) of these profiles will enhance the object relative to this clutter and system noise. When the object is moving, however, both the object and the clutter are averaged toward zero. The use of post-detection integration (non-coherent averaging using second-order statistics) with these profiles when moving objects are present, results in averages smeared by the motion. Other methods, such as track-before-detect, are used with non-coherent averaging to try to compensate for this motion. With this approach, a series of target trajectories is postulated and tested. The problem with this method is that many processing stages are required, and, since the phase is lost, the second-order averages contain less target information.
For example, coherent averaging techniques could, in theory, enhance the signal to noise ratio (SNR) of a returned signal just as effectively for a moving target as for a stationary one if the trajectory of the target were known. Ideally, in such a case, the returned signals could be first processed to compensate for the target motion and averaging carried out on the compensated signals. However, this is seldom the case and attempts at such an approach have generally involved postulating a plurality of trajectories of the target, performing the averaging processing for each of the postulated trajectories and then simply choosing the result with the highest final SNR which will occur for the postulated trajectory which most closely matches the actual trajectory. This technique is computationally intensive and, as a practical matter, does not guarantee enhancement of the signal to noise ratio since small errors of uncompensated motion cause rapid loss of the benefits of averaging techniques.
Many techniques are also known for improvement of signal to noise ratio in image processing by correlation of image samples or image frames. However, in such applications the data generally represents a real image such as a map of intensity values rather than signals which have yet to be reduced to an image, as in the case of radar and sonar. For example, in the publication "Shift and Rotation Invariant Object Reconstruction Using the Bispectrum" by Brian M Sadler, presented at a "Workshop on Higher-Order Spectral Analysis", sponsored by the Office of Naval Research and the National Science Foundation in cooperation with several IEEE societies, Jun. 28-30, 1989, a technique of image extraction from 10 frames containing a binary image and white Gaussian noise at an SNR of -10 db by using averaging of a triple correlation (bispectrum) was presented. However, this technique is not applicable to radar or sonar signals which are complex (e.g. contain phase information) since the phase information would cause averaging of the triple correlation to go to zero by the technique disclosed therein.
Even though it is known that processing of higher order spectra (bispectra, trispectra, etc. involving correlation by forming a product of more than two variables) is capable of preserving phase information, as taught by "Advances in Spectrum Analysis and Array Processing, Volume I", Simon Haykin, Editor: Prentice-Hall, 1991, and an article entitled "Higher-Order Spectral Analysis" by Chrysostomos L. Nikias, included therein, and the possibility of improvement of SNR of stochastic signals was recognized, a practical technique of extraction or reconstruction of high resolution images of a moving radar target from a noisy signal has not, heretofore, been achieved.
It should also be recognized in this regard, that target motion includes the relative motion of the transmitter of energy and/or receiver of reflected energy relative to the target and along the energy transmission and return paths. Therefore, degradation of the high resolution image can occur in many desirable applications such as vibration in aircraft, body motion in sonograms, and motion of density gradients of the surrounding medium in atmospheric and marine environments. Therefore, the inability to obtain high resolution images or profiles of moving targets has been a major limitation on high resolution radar in particular.
A solution to the improvement of signal to noise ratio (SNR) is provided in the above incorporated patent application involving the estimation of a position invariant portion of the trispectrum, referred to as a trispectral slice which may be averaged and the profile corresponding to that average recreated by performing a Fourier transform on that slice. However, due to the position invariant properties of that trispectral slice estimation, velocity information is suppressed. Therefore, such velocity information could only be obtained by other radar signals and/or radar signal processing techniques. Since the high precision information available from high resolution radar is computationally intensive, the performance of different computations on other data which does not suppress position or velocity information has substantially complicated radar imaging and has required objectionable amounts of time in order to extract both the profile of a collective target and its velocity.